Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for moving an autonomous vehicle among a plurality of vehicles configured on a railway track system, the method comprising: initiating movement of the autonomous vehicle configured in a rail of the railway system, the autonomous vehicle comprising a sensor array system configured spatially on the autonomous vehicle (AV), the sensor array system comprising a plurality of active sensor systems; at least one processor device coupled to the sensor array system; a memory device coupled to the processing device; an instruction stored on the memory device, the instruction when executed by the processor causes the sensor array system to, as the AV travels a current route on the rail of the railway track system, dynamically detect a reflectance of an event from a plurality of events, the event being selected from an anomaly, a stationary feature, or a location of one of a plurality of vehicles configured on the rail of the railway system; and using data from the reflectance of the event or the plurality of events to adjust a movement of the AV in relationship to the event, while the AV is mechanically disconnected from the plurality of vehicles configured on the rail of the railway system; and monitoring each of the AVs using a central database in real time, while collecting information associated each AVs in the central database; moving the AV from the rail of the railway system to an adjacent system configured with the rail, the adjacent system comprising at least one of a roadway, a waterway, or any combination thereof, such that the processor device coupled to the sensor array system executes an instruction stored on the memory device causing the sensor array system to, as the AV travels a current route on the adjacent system, dynamically detect a reflectance of an event from a plurality of events, the event being selected from an anomaly, a stationary feature, or a location of one of the plurality of vehicles; and using data from the reflectance of the event or the plurality of events to adjust a movement of the AV in relationship to the event, while the AV is mechanically disconnected from the rail of the railway system; and using a cell phone to communicate with the AV to contact the AV for a pickup by a user before the initiating movement of the AV configured on the rail of the railway system.
An autonomous vehicle (AV) system for railway and multi-modal transportation involves an AV equipped with a sensor array system, including multiple active sensors, at least one processor, and a memory device. The AV operates on a railway track system, dynamically detecting reflectance from events such as anomalies, stationary features, or other vehicles. The sensor data is used to adjust the AV's movement while it remains mechanically disconnected from other vehicles on the tracks. A central database monitors all AVs in real time, collecting operational data. The AV can transition from the railway to an adjacent system, such as a roadway or waterway, where the sensor array continues to detect reflectance from events to adjust movement. Additionally, users can communicate with the AV via a cell phone to request pickup before the AV initiates movement on the railway. This system enables autonomous navigation across different transportation modes while ensuring real-time monitoring and adaptive movement based on environmental and vehicle detection.
2. The method of claim 1 wherein the AV further comprises: a relationship table comprising a plurality of sensor configurations for each respective one of a plurality of active sensor systems included in the sensor array system to adjust within a time frame at least one output sensor configuration for the sensor array to identify the event using the detected reflectance of the event; and an output to influence movement of the AV based upon the detected reflectance and identified event as the AV travels the current route.
This invention relates to autonomous vehicle (AV) systems equipped with sensor arrays for event detection and route navigation. The problem addressed is the need for adaptive sensor configurations to accurately identify events (e.g., obstacles, road conditions) based on reflectance data while dynamically adjusting the AV's movement. The system includes a sensor array with multiple active sensor systems, each configurable to detect reflectance from events along the AV's path. A relationship table stores multiple sensor configurations for each sensor system, allowing real-time adjustments to optimize event identification. The system analyzes detected reflectance data to identify events and selects the most effective sensor configuration within a defined time frame. Based on the identified event and reflectance data, the system generates an output signal to influence the AV's movement, ensuring safe and efficient navigation along the current route. The adaptive sensor configurations improve event detection accuracy by dynamically adjusting to varying environmental conditions, while the output signal ensures timely responses to detected events. This enhances the AV's ability to navigate complex environments by leveraging reflectance data and configurable sensor systems.
3. The method of claim 2 wherein sensor array system comprises a light-detection and ranging (LiDAR) system, and wherein the output sensor configuration configures one or more of a scan rate of the LiDAR system, a photodetector sensitivity of the LiDAR system, or a laser power level of the LiDAR system.
LiDAR systems are used for remote sensing by emitting laser pulses and measuring the reflected light to determine distances and create 3D maps. However, optimizing LiDAR performance for different environments and applications remains challenging. A method addresses this by dynamically configuring a LiDAR system based on real-time sensor data. The system adjusts operational parameters such as scan rate, photodetector sensitivity, or laser power level to enhance accuracy, efficiency, or power consumption. For example, in low-light conditions, the photodetector sensitivity may be increased to improve signal detection, while in high-reflectivity environments, the laser power level may be reduced to avoid sensor saturation. The configuration is determined by analyzing input data from the LiDAR system or other sensors, ensuring adaptive performance without manual intervention. This approach optimizes LiDAR functionality for diverse scenarios, improving reliability and reducing energy use.
4. The method of claim 2 wherein sensor array system comprises a radar system, and wherein the output sensor configuration configures a pulse width of a carrier signal of the radar system.
A radar system is used to detect and track objects by transmitting electromagnetic waves and analyzing the reflected signals. A key challenge in radar systems is optimizing the pulse width of the carrier signal to balance range resolution and signal-to-noise ratio. A narrower pulse width improves resolution but reduces energy per pulse, while a wider pulse width increases energy but lowers resolution. This trade-off must be carefully managed to ensure accurate detection and tracking. The invention addresses this challenge by dynamically configuring the pulse width of the radar system's carrier signal based on sensor output. The system includes a sensor array that monitors environmental or operational conditions, such as target distance, clutter, or interference levels. The sensor output is processed to determine an optimal pulse width configuration. For example, if the target is close, a narrower pulse width may be selected to enhance resolution, while a wider pulse width may be used for distant targets to improve signal strength. The system adjusts the pulse width in real-time to adapt to changing conditions, ensuring efficient and accurate radar performance. This approach improves detection accuracy, reduces false alarms, and optimizes energy usage.
5. The method of claim 2 wherein each of the plurality of active sensor systems emit one or more of sounds waves or electromagnetic waves.
This invention relates to a system for monitoring and analyzing environmental conditions using multiple active sensor systems. The system addresses the challenge of accurately detecting and characterizing objects or conditions in dynamic environments by employing sensors that actively emit signals, such as sound waves or electromagnetic waves, to probe the surroundings. These emitted signals interact with the environment, and the resulting reflections or responses are captured and analyzed to derive information about the presence, location, or properties of objects or phenomena of interest. The active sensor systems are distributed across a monitored area, each capable of emitting and receiving signals to detect changes or anomalies. The emitted signals may include sound waves, such as ultrasonic or audible frequencies, or electromagnetic waves, such as radio waves, microwaves, or optical signals. By analyzing the returned signals, the system can determine characteristics like distance, material composition, or movement patterns. The system may also incorporate signal processing techniques to filter noise, enhance resolution, or improve detection accuracy. The invention further includes a processing unit that interprets the sensor data to generate actionable insights, such as identifying intrusions, monitoring environmental changes, or detecting structural anomalies. The system may be configured for applications in security, industrial monitoring, or environmental sensing, where real-time or continuous data acquisition is essential. The use of multiple active sensors ensures redundancy and improves coverage, reducing blind spots and increasing reliability in diverse operational conditions.
6. The method of claim 2 wherein the executed instruction causes the system to dynamically determine the one or more output sensor configurations by performing a lookup in the relationship table based on a reflectance of each of the detected reflectance events.
This invention relates to a system for dynamically configuring output sensor configurations based on detected reflectance events. The system addresses the challenge of optimizing sensor performance in environments where reflectance properties vary, ensuring accurate and reliable data collection. The method involves detecting reflectance events from a target surface, where each event corresponds to a specific reflectance property. A relationship table is used to map these reflectance properties to optimal output sensor configurations. The system dynamically determines the appropriate sensor configuration by performing a lookup in the relationship table based on the reflectance of each detected event. This ensures that the sensor operates at peak efficiency for the given conditions, improving data accuracy and system reliability. The relationship table may be pre-populated with configurations optimized for different reflectance scenarios, allowing the system to adapt in real-time without manual intervention. This dynamic adjustment is particularly useful in applications where reflectance properties change frequently, such as in industrial inspection, medical imaging, or environmental monitoring. The method enhances sensor adaptability and reduces the need for manual calibration, leading to more consistent and precise measurements.
7. The method of claim 2 wherein the executed instruction cause the system to dynamically determine the one or more output sensor configurations by performing an optimization utilizing a plurality of possible configurations for each of the plurality of active sensor systems based on a reflectance of each of the detected reflectance events by identifying a surface feature of each of the events, and wherein the optimization uses a fitting function to converge on the one or more sensor output configurations in the relationship table.
This invention relates to sensor systems used in imaging or detection applications, particularly for optimizing sensor configurations based on reflectance data. The problem addressed is the need to dynamically adjust sensor configurations to improve accuracy and efficiency in detecting surface features from reflectance events. Traditional systems may rely on fixed configurations, which can lead to suboptimal performance in varying conditions. The invention involves a method for dynamically determining optimal sensor configurations for a plurality of active sensor systems. The method analyzes reflectance events detected by the sensors, identifying surface features associated with each event. An optimization process is then performed using a plurality of possible configurations for each sensor system. The optimization utilizes a fitting function to evaluate the reflectance of each event, converging on the most suitable sensor output configurations. These configurations are stored in a relationship table, allowing the system to adapt in real-time to different surface features and reflectance conditions. The optimization process ensures that the selected configurations maximize the accuracy and reliability of the sensor outputs, improving overall system performance. This dynamic adjustment capability enhances the system's ability to handle diverse environments and applications, such as industrial inspection, medical imaging, or autonomous navigation.
8. The method of claim 2 wherein the plurality of active sensor systems comprise a LiDAR system and a radar system, and wherein the sensor array further includes a plurality of passive sensor systems that detect reflected natural light.
This invention relates to advanced sensor systems for environmental monitoring and data collection. The technology addresses the challenge of accurately detecting and analyzing objects or conditions in dynamic environments by integrating multiple sensor modalities to improve detection accuracy and reliability. The system combines active and passive sensor systems to capture comprehensive data. Active sensor systems, such as LiDAR and radar, emit signals to detect and measure distances, velocities, or other properties of objects. LiDAR uses laser pulses to create high-resolution 3D maps, while radar employs radio waves to track moving objects. Passive sensor systems detect reflected natural light, such as from the sun or other ambient sources, to provide additional visual or spectral data without emitting signals. The combination of these sensors enhances detection capabilities by compensating for the limitations of individual systems, such as LiDAR's susceptibility to weather interference or radar's lower resolution. The passive sensors supplement the active systems by providing contextual or supplementary data, improving overall system robustness. This multi-modal approach is particularly useful in applications like autonomous navigation, environmental monitoring, or industrial inspection, where accurate and reliable sensing is critical. The system dynamically processes data from all sensors to generate a unified output, enabling real-time decision-making or analysis.
9. The method of claim 8 wherein the at least one of passive sensor systems of the sensor array comprise a stereo camera system.
A system and method for environmental monitoring and analysis utilizes a sensor array to detect and track objects in a monitored area. The sensor array includes at least one passive sensor system, such as a stereo camera system, which captures visual data without emitting signals. The stereo camera system operates by capturing images from two or more spatially separated viewpoints, enabling depth perception and three-dimensional reconstruction of the monitored environment. This allows for accurate object detection, tracking, and analysis without requiring active illumination or signal transmission, reducing power consumption and minimizing interference with other systems. The system processes the captured stereo images to generate depth maps, which are used to determine the position, movement, and characteristics of objects within the monitored area. The passive nature of the stereo camera system ensures that it does not emit any signals that could be detected by external devices, enhancing security and operational stealth. The system may also integrate additional sensor types, such as thermal or acoustic sensors, to provide a comprehensive monitoring solution. The method involves continuously capturing stereo images, processing the data to extract relevant information, and updating the system's understanding of the environment in real time. This approach is particularly useful in applications where unobtrusive monitoring is required, such as surveillance, autonomous navigation, or environmental monitoring.
10. The method of claim 9 wherein the LiDAR system, the radar system, and the stereo camera system each provides sensor data to a control system of the AV to enable the AV to maneuver along the current route and initiate adjustment of the movement of the AV along the track.
This invention relates to autonomous vehicle (AV) navigation systems that integrate multiple sensor inputs to improve maneuvering and route adjustment. The system combines data from a LiDAR system, a radar system, and a stereo camera system to enhance situational awareness and decision-making. The LiDAR system provides high-resolution 3D mapping of the environment, detecting obstacles and terrain features with precision. The radar system supplements this with long-range detection and velocity tracking of moving objects, ensuring reliable performance in varying weather conditions. The stereo camera system captures stereo images to derive depth information and identify visual cues, such as lane markings and traffic signs, for navigation. The sensor data from all three systems is processed by a control system, which uses the combined inputs to maneuver the AV along a predefined route. The control system also initiates adjustments to the AV's movement based on real-time sensor feedback, allowing for dynamic responses to changing conditions. This multi-sensor fusion approach improves the AV's ability to navigate complex environments safely and efficiently.
11. The method of claim 10 wherein the control system dynamically processes data from the sensor array system to increase speed, reduce speed, or stop the AV along the current route or wherein the control system interfaces with the central database to increase speed, reduce speed, or stop the AV along the current route.
This invention relates to autonomous vehicle (AV) control systems that dynamically adjust vehicle speed or stop the vehicle based on real-time data processing. The system includes a sensor array that collects environmental and operational data, such as traffic conditions, road hazards, or vehicle performance metrics. A control system processes this data to determine optimal speed adjustments or stopping actions along the vehicle's current route. Additionally, the control system can interface with a central database to access additional data, such as traffic patterns, weather conditions, or infrastructure updates, to further refine speed or stopping decisions. The system ensures safe and efficient AV operation by dynamically responding to both real-time sensor inputs and centralized data sources. This approach enhances situational awareness and decision-making, improving overall AV performance and safety.
12. The method of claim 11 wherein the detected reflectance events comprise one or more surface features of an environment around the AV and one or more weather features indicating precipitation.
This invention relates to autonomous vehicle (AV) systems that detect and analyze reflectance events to improve navigation and safety. The method involves capturing sensor data, such as lidar or camera inputs, to identify reflectance events in the AV's environment. These events include surface features like road markings, obstacles, or terrain variations, as well as weather-related features indicating precipitation, such as rain, snow, or fog. The system processes the detected reflectance events to distinguish between environmental features and weather conditions, enabling the AV to adjust its navigation strategies accordingly. For example, if precipitation is detected, the AV may modify its speed, braking, or path planning to account for reduced visibility or slippery surfaces. The method enhances the AV's ability to operate safely in dynamic and adverse conditions by providing real-time environmental awareness. The system may also integrate multiple sensor modalities to improve detection accuracy and reliability. This approach ensures that the AV can adapt to changing conditions, improving overall performance and safety.
13. The method of claim 12 wherein the executed instruction further causes the predictive sensor array configuration system to: in response to identifying the weather features indicating precipitation, deprioritize sensor data from the LiDAR system for processing by the control system.
This invention relates to a predictive sensor array configuration system for autonomous vehicles, addressing the challenge of optimizing sensor data processing in varying weather conditions. The system dynamically adjusts sensor prioritization based on real-time weather data to improve vehicle control and safety. The system monitors weather features, such as precipitation, and modifies sensor data processing accordingly. When precipitation is detected, the system deprioritizes LiDAR sensor data, reducing its influence on the vehicle's control system. This adjustment helps mitigate LiDAR performance degradation in rain or snow, where optical sensors may produce unreliable or noisy data. Instead, the system may prioritize other sensors, such as radar or cameras, which are less affected by precipitation. The invention enhances autonomous vehicle reliability by adapting sensor inputs to environmental conditions, ensuring safer and more efficient operation. The system integrates weather data analysis with sensor management, allowing real-time adjustments to maintain optimal control system performance. This approach improves decision-making in adverse weather, reducing the risk of errors caused by degraded sensor inputs.
14. The method of claim 12 wherein the executed instruction further causes the system to: in response to identifying the weather features indicating precipitation, deactivate the LiDAR system.
This invention relates to autonomous vehicle systems that use LiDAR (Light Detection and Ranging) sensors for navigation and obstacle detection. A key challenge in such systems is ensuring reliable sensor operation under varying environmental conditions, particularly adverse weather like rain or snow, which can degrade LiDAR performance by scattering or absorbing laser pulses. The invention addresses this by dynamically adjusting LiDAR operation based on detected weather conditions to maintain system accuracy and safety. The system includes a LiDAR sensor, a weather detection module, and a processing unit. The weather detection module identifies precipitation events by analyzing sensor data, such as optical or radar inputs, to detect rain, snow, or other precipitation. When precipitation is detected, the system deactivates the LiDAR sensor to prevent erroneous readings caused by weather interference. This ensures the autonomous vehicle relies on alternative sensors, such as cameras or radar, which may be less affected by precipitation. The system may also reactivate the LiDAR sensor once weather conditions improve, as determined by continued monitoring. This adaptive approach enhances sensor reliability and vehicle safety in dynamic environments.
15. The method of claim 2 wherein the executed instruction causes the system to dynamically identify the one or more reflectance events by receiving reflectance data from a number of proximate AVs traveling on the current route on the railway system.
This invention relates to railway systems and addresses the challenge of dynamically identifying reflectance events, such as track obstructions or anomalies, to enhance safety and efficiency. The method involves using reflectance data collected from multiple autonomous vehicles (AVs) operating on the same railway route. Each AV is equipped with sensors that detect and transmit reflectance data, which is then analyzed to identify potential issues. The system dynamically processes this data in real-time, allowing for rapid detection of reflectance events that could indicate track problems. By aggregating data from multiple AVs, the system improves accuracy and reliability compared to relying on a single vehicle's observations. This approach enables proactive maintenance and reduces the risk of accidents caused by unnoticed track conditions. The method is particularly useful in automated railway systems where continuous monitoring is essential for safe and efficient operation. The invention ensures that reflectance events are promptly identified and addressed, enhancing overall railway safety and performance.
16. The method of claim 15 wherein the executed instruction causes the system to receive the reflectance data from the proximate AVs by establishing a mesh network with the proximate AVs on the railway system or other entities that are not AVs.
This invention relates to a system for collecting and analyzing reflectance data from autonomous vehicles (AVs) operating on a railway system. The railway system includes multiple AVs equipped with sensors that measure reflectance data, such as light or signal reflections, to aid in navigation and collision avoidance. The problem addressed is the need for efficient and reliable data sharing among AVs to improve situational awareness and safety. The method involves executing an instruction that enables a system to receive reflectance data from nearby AVs by establishing a mesh network. This mesh network allows direct communication between AVs, facilitating real-time data exchange without relying on a centralized infrastructure. The network can also include non-AV entities, such as fixed railway infrastructure or other connected devices, to enhance data collection and coordination. By sharing reflectance data, AVs can better detect obstacles, track conditions, and optimize routing, reducing the risk of accidents and improving operational efficiency. The mesh network ensures robust communication even in areas with limited connectivity, making the system adaptable to various railway environments.
17. The method of claim 2 wherein the executed instruction further causes the system to: maintain a sub-map database comprising 3D surface data of an operational region of the AV on the railway system; and identify, using a current position of the AV, a correlated sub-map from the sub-map database that provides 3D surface data surrounding the current position of the AV; wherein the executed instruction causes the system to dynamically identify the reflectance events that affect detectability by the sensor array from the 3D surface data provided by the correlated sub-map.
This invention relates to autonomous vehicle (AV) navigation on railway systems, specifically addressing challenges in sensor-based detection and obstacle avoidance. The system uses a sensor array to detect reflectance events, which are light or signal reflections that can interfere with accurate obstacle detection. To improve reliability, the system maintains a sub-map database containing detailed 3D surface data of the operational region of the AV on the railway system. This database includes high-resolution surface characteristics that can influence sensor performance. As the AV moves, the system dynamically identifies its current position and retrieves a correlated sub-map from the database, providing localized 3D surface data around the AV. The system then analyzes this data to predict how reflectance events will affect the detectability of obstacles by the sensor array. By correlating real-time sensor data with pre-mapped surface conditions, the system enhances obstacle detection accuracy and reduces false positives or negatives caused by environmental factors. This approach ensures safer and more reliable autonomous navigation in railway environments.
18. The method of claim 2 wherein the stationary feature being one or more of a railway sign, a railway station, a railway track, a vehicle roadway, a railway crossing, or other fixture, the location of one of the other plurality of vehicles comprising a distance between the other vehicle and the AV, the anomaly being one or more of a human being, a dog, a cat, a horse, cattle, a moving vehicle crossing the railway track, a weather condition, or a defect on the railway track.
This invention relates to autonomous vehicle (AV) systems designed to detect and respond to anomalies in railway environments. The technology addresses the challenge of ensuring safe operation of AVs near railway infrastructure by identifying potential hazards that could disrupt railway operations or endanger passengers and pedestrians. The system monitors the surroundings of an AV to detect stationary features such as railway signs, stations, tracks, roadways, crossings, or other fixtures. It also tracks the location of nearby vehicles, measuring the distance between these vehicles and the AV. The system identifies anomalies, which may include living beings (e.g., humans, animals like dogs, cats, horses, or cattle), moving vehicles crossing railway tracks, adverse weather conditions, or defects on the railway tracks. By detecting these anomalies, the AV can take appropriate actions to avoid collisions, ensure compliance with railway safety protocols, and maintain smooth operation in dynamic railway environments. The invention enhances railway safety by providing real-time hazard detection and response capabilities for autonomous vehicles operating in proximity to railway infrastructure.
19. A method for initiating a call from a user using a network and moving an autonomous vehicle among a plurality of vehicles configured on a railway track system, the method comprising: initiating movement of the autonomous vehicle configured in a rail of the railway system, the autonomous vehicle comprising a sensor array system configured spatially on the autonomous vehicle (AV), the sensor array system comprising a plurality of active sensor systems; at least one processor device coupled to the sensor array system; a memory device coupled to the processing device; an instruction stored on the memory device, the instruction when executed by the processor causes the sensor array system to, as the AV travels a current route on the rail of the railway track system, dynamically detect a reflectance of an event from a plurality of events, the event being selected from an anomaly, a stationary feature, or a location of a plurality of other vehicles; and using data from the reflectance of the event or the plurality of events to adjust a movement of the AV in relationship to the event, while the AV is mechanically disconnected from the plurality of other vehicles configured on the rail of the railway system or the AV is mechanically connected to one or N−1 of the plurality of vehicles numbered from 2 to N.
This invention relates to autonomous vehicle (AV) navigation and control within a railway track system. The problem addressed is the need for autonomous vehicles to dynamically detect and respond to various events, such as anomalies, stationary features, or other vehicles, while operating on railway tracks, whether connected or disconnected from other vehicles. The solution involves an autonomous vehicle equipped with a sensor array system comprising multiple active sensors. The vehicle includes a processor and memory storing instructions that, when executed, enable the sensor array to dynamically detect reflectance from events along the track. The detected reflectance data is used to adjust the vehicle's movement in real-time, ensuring safe and efficient navigation. The system operates whether the AV is mechanically disconnected from other vehicles or connected to one or more vehicles in a train formation. The sensor array continuously monitors the environment, allowing the AV to respond to obstacles, track conditions, or other vehicles, enhancing safety and operational efficiency in railway systems.
20. The method of claim 19 further comprising a light-detection and ranging (LiDAR) system included in the sensor array system, the LiDAR being configured with the output sensor configuration to adjust one or more of a scan rate of the LiDAR system, a photodetector sensitivity of the LiDAR system, or a laser power level of the LiDAR system; a relationship table comprising a plurality of sensor configurations for each respective one of a plurality of active sensor systems included in the sensor array system to adjust within a time frame at least one output sensor configuration for the sensor array to conclusively identify the event using the detected reflectance of the event; an output to influence movement of the AV based upon the detected reflectance and identified event as the AV travels the current route; and a control system coupled to the processor to dynamically processes data from the output derived from the sensor array system to increase speed, reduce speed, or stop the AV along the current route.
This invention relates to autonomous vehicle (AV) sensor systems designed to improve event detection and response. The system includes a sensor array with a LiDAR system that dynamically adjusts operational parameters such as scan rate, photodetector sensitivity, or laser power to optimize event detection. The system also uses a relationship table to configure multiple active sensors within the array, adjusting their output configurations in real-time to accurately identify events based on detected reflectance. The detected reflectance and identified event data are used to generate outputs that influence the AV's movement, such as increasing speed, reducing speed, or stopping. A control system processes these outputs to dynamically adjust the AV's actions along its current route, enhancing safety and responsiveness. The invention addresses the challenge of reliably detecting and responding to events in varying environmental conditions by dynamically configuring sensor parameters and leveraging reflectance data for precise event identification. This ensures the AV can navigate safely and efficiently by adapting its sensor configurations and control actions in real-time.
21. The method of claim 19 wherein the railway system can be selected from a rail road system, a trolley system, or other rail or fixed route system using a rail or cables.
This invention relates to a method for managing a railway system, which can include railroads, trolley systems, or other rail-based or fixed-route transportation systems that use rails or cables. The method addresses the challenge of efficiently monitoring and controlling such systems to ensure safe and reliable operation. The system involves a network of sensors and communication devices distributed along the tracks or routes to collect real-time data on track conditions, vehicle positions, and environmental factors. This data is processed by a central control unit that analyzes the information to detect anomalies, predict maintenance needs, and optimize scheduling. The method also includes automated adjustments to system parameters, such as speed limits or route assignments, based on the collected data. Additionally, the system may incorporate predictive algorithms to anticipate potential failures or disruptions, allowing for proactive maintenance and minimizing downtime. The invention aims to improve safety, reduce operational costs, and enhance overall efficiency in railway and similar fixed-route transportation systems.
22. A system for initiating movement and adjusting the movement of an autonomous vehicle configured on a rail of a railway track system, the system comprising: a plurality of vehicles configured on the rail of the railway system and the autonomous vehicle (“AV”) configured on a rail of the railway system, and mechanically detached from the plurality of vehicles, the AV comprising: a sensor array system configured spatially on the autonomous vehicle (AV), the sensor array system comprising a plurality of active sensor systems; at least one processor device coupled to the sensor array system; a memory device coupled to the processing device; a first instruction stored on the memory device, the first instruction when executed by the processor causes the sensor array system to, as the AV travels a current route on a rail of the railway track system, dynamically detect a reflectance of an event from a plurality of events, the event being selected from an anomaly, a stationary feature, or a location of one of a plurality of vehicles; a second instruction stored on the memory device, the second instruction when executed by the processor causes the sensor array system to, as the AV travels a current route on an adjacent system, the adjacent system comprising at least one of a roadway, a waterway, or a combination of the roadway and the waterway, dynamically detect a reflectance of an event from a plurality of events, the event being selected from an anomaly, a stationary feature, or a location of one of the plurality of vehicles; a relationship table comprising a plurality of sensor configurations for each respective one of a plurality of active sensor systems included in the sensor array system to adjust within a time frame at least one output sensor configuration for the sensor array to identify the event using the detected reflectance of the event; and an output to influence movement of the AV based upon the detected reflectance and identified event as the AV travels the current route; and an application configured on a cell phone coupled to a networking system, the application on the cell phone of the user provided to initiate pickup of the user using the AV.
An autonomous vehicle (AV) system operates on a railway track system, where the AV is mechanically detached from other vehicles on the same rail. The AV includes a sensor array system with multiple active sensors to dynamically detect reflectance from events such as anomalies, stationary features, or the locations of other vehicles. The system processes these detections to identify events and adjust sensor configurations in real-time to improve detection accuracy. The AV can also detect reflectance from adjacent systems like roadways or waterways. A relationship table stores sensor configurations for different scenarios, allowing the system to optimize sensor output for event identification. The AV's movement is influenced based on the detected events and their reflectance. Additionally, a mobile application allows users to request the AV for pickup, integrating user interaction with the autonomous transportation system. The system enhances safety and efficiency by dynamically adapting to environmental conditions and user demands.
23. The system of claim 22 , wherein sensor array system comprises a light-detection and ranging (LiDAR) system, and wherein the output sensor configuration configures one or more of a scan rate of the LiDAR system, a photodetector sensitivity of the LiDAR system, or a laser power level of the LiDAR system.
The system involves a sensor array system integrated with a light-detection and ranging (LiDAR) system for environmental perception and data acquisition. The LiDAR system emits laser pulses and measures the time-of-flight to detect and map objects in the surrounding environment, addressing challenges in autonomous navigation, obstacle detection, and 3D mapping. The system includes an output sensor configuration that dynamically adjusts operational parameters of the LiDAR system to optimize performance under varying conditions. Specifically, the configuration modifies the scan rate, which determines how frequently the LiDAR system updates its measurements, ensuring real-time responsiveness. It also adjusts the photodetector sensitivity to enhance signal detection in low-light or high-reflectivity scenarios, improving accuracy. Additionally, the laser power level can be configured to balance energy efficiency and detection range, reducing power consumption while maintaining sufficient coverage. These adjustments enable the LiDAR system to adapt to different environmental factors, such as weather, lighting, and object reflectivity, ensuring reliable and efficient operation in diverse applications like autonomous vehicles, robotics, and industrial automation.
24. The system of claim 22 , wherein sensor array system comprises a radar system, and wherein the output sensor configuration configures a pulse width of a carrier signal of the radar system.
A system for radar-based sensing includes a sensor array system that comprises a radar system. The radar system is configured to generate and transmit a carrier signal, and the system includes an output sensor configuration that adjusts the pulse width of the carrier signal. The pulse width configuration allows for optimization of the radar system's performance based on specific sensing requirements, such as range resolution or signal-to-noise ratio. The radar system may operate in various frequency bands and can be used for applications like object detection, tracking, or imaging. The sensor array system may also include additional sensors or components that work in conjunction with the radar system to enhance sensing capabilities. The output sensor configuration dynamically adjusts the pulse width to adapt to different environmental conditions or target characteristics, improving the accuracy and reliability of the radar system's measurements. This configuration ensures that the radar system operates efficiently while maintaining high performance in diverse scenarios.
25. The system of claim 22 , wherein each of the plurality of active sensor systems emit one or more of sounds waves or electromagnetic waves.
The system relates to active sensor systems used for environmental monitoring, surveillance, or data collection. The problem addressed is the need for versatile and adaptable sensing capabilities to detect and analyze various types of signals in different environments. Traditional passive sensor systems rely on detecting existing signals, which may be limited in range or accuracy. Active sensor systems, which emit their own signals, can overcome these limitations by actively probing the environment. The system includes multiple active sensor systems, each capable of emitting sound waves, electromagnetic waves, or both. These emissions allow the sensors to interact with the environment, such as reflecting off objects or penetrating materials, to gather detailed data. The emitted waves can be tailored to specific applications, such as ultrasonic waves for material testing or radio waves for communication or radar. The system dynamically adjusts the type and frequency of emitted waves based on environmental conditions or operational requirements, improving accuracy and reliability. The sensors may also incorporate signal processing to analyze received reflections or responses, enabling real-time data interpretation. This adaptability makes the system suitable for diverse applications, including industrial inspections, environmental monitoring, and security surveillance. The use of multiple sensor systems ensures comprehensive coverage and redundancy, enhancing overall system robustness.
26. The system of claim 22 , wherein the executed instruction causes the system to dynamically determine the one or more output sensor configurations by performing a lookup in the relationship table based on a reflectance of each of the detected reflectance events.
The invention relates to a system for dynamically configuring output sensor configurations based on detected reflectance events. The system addresses the challenge of optimizing sensor performance in varying environmental conditions by dynamically adjusting sensor settings in response to real-time reflectance data. The system includes a relationship table that maps reflectance values to specific sensor configurations, allowing for adaptive adjustments. When a reflectance event is detected, the system performs a lookup in the relationship table using the reflectance value of the event to determine the appropriate output sensor configuration. This dynamic adjustment ensures that the sensor operates optimally under different conditions, improving accuracy and reliability. The system may also include components for detecting reflectance events, processing the detected data, and applying the determined configurations to the sensor. The relationship table is pre-populated with data that correlates reflectance values to optimal sensor settings, enabling the system to make rapid, precise adjustments without manual intervention. This approach enhances sensor performance in applications where environmental conditions frequently change, such as in industrial automation, medical imaging, or environmental monitoring.
27. The system of claim 22 , wherein the executed instruction cause the system to dynamically determine the one or more output sensor configurations by performing an optimization utilizing a plurality of possible configurations for each of the plurality of active sensor systems based on a reflectance of each of the detected reflectance events by identifying a surface feature of each of the events, and wherein the optimization uses a fitting function to converge on the one or more sensor output configurations in the relationship table.
This invention relates to a system for dynamically optimizing sensor configurations in a multi-sensor environment, particularly for applications involving reflectance-based surface feature detection. The system addresses the challenge of efficiently determining optimal sensor configurations in real-time to improve accuracy and performance in detecting and analyzing surface features from reflectance events. The system includes multiple active sensor systems that detect reflectance events from a surface. Each detected event is analyzed to identify specific surface features based on reflectance properties. The system then dynamically determines the optimal sensor output configurations by performing an optimization process. This optimization evaluates a plurality of possible configurations for each active sensor system, using a fitting function to converge on the most suitable configurations. The fitting function assesses the reflectance of each detected event, ensuring that the selected configurations accurately represent the surface features. The optimized configurations are stored in a relationship table, which maps the best-performing configurations to the detected events. This dynamic adjustment enhances the system's ability to adapt to varying surface conditions and improve detection accuracy. The optimization process ensures that the sensor outputs are consistently aligned with the identified surface features, improving overall system performance.
28. The system of claim 22 , wherein the plurality of active sensor systems comprise a LiDAR system and a radar system, and wherein the sensor array further includes a plurality of passive sensor systems that detect reflected natural light.
This invention relates to a sensor system for environmental perception, particularly for autonomous vehicles or robotic systems. The system addresses the challenge of accurately detecting and interpreting surroundings using multiple sensor modalities to improve reliability and robustness in varying conditions. The system includes a combination of active and passive sensor systems. The active sensors comprise a LiDAR system, which emits laser pulses to measure distances and create high-resolution 3D maps of the environment, and a radar system, which uses radio waves to detect objects and measure their velocity. These active sensors provide precise spatial and motion data but may be limited by environmental factors like weather or interference. To complement the active sensors, the system also incorporates passive sensor systems that detect reflected natural light, such as cameras or optical sensors. These passive sensors capture visual information without emitting signals, making them less susceptible to certain environmental disruptions. By integrating both active and passive sensors, the system enhances environmental awareness, reducing blind spots and improving overall detection accuracy. The combined data from these sensors is processed to generate a comprehensive understanding of the surroundings, enabling applications in autonomous navigation, obstacle avoidance, and real-time decision-making. The system's multi-modal approach ensures redundancy and improves performance in diverse operating conditions.
29. The system of claim 22 , wherein the at least one of passive sensor systems of the sensor array comprise a stereo camera system.
A system for environmental monitoring and analysis includes a sensor array with multiple passive sensor systems, such as cameras, microphones, or other non-intrusive devices, to capture data from a monitored environment. The system processes this data to detect, track, and analyze objects, activities, or conditions within the environment. One of the passive sensor systems in the array is a stereo camera system, which captures depth information by using two or more cameras to generate three-dimensional data. This stereo vision capability enhances object detection, motion tracking, and spatial analysis, allowing the system to accurately determine distances, positions, and movements of objects in the monitored area. The system may integrate data from the stereo camera with other sensors to improve accuracy and reliability. Applications include surveillance, industrial automation, autonomous navigation, and environmental monitoring, where passive sensing is preferred to avoid interference with the monitored environment. The stereo camera system provides detailed spatial information without requiring active illumination or physical contact, making it suitable for scenarios where minimal intrusion is desired.
30. The system of claim 22 , wherein the LiDAR system, the radar system, and the stereo camera system each provides sensor data to a control system of the AV to enable the AV to maneuver along the current route and initiate adjustment of the movement of the AV along the track.
The system is designed for autonomous vehicles (AVs) to navigate and adjust movement along a predefined route. The system integrates multiple sensor technologies—LiDAR, radar, and stereo cameras—to collect environmental data. Each sensor system provides distinct data to a central control system, which processes the information to enable precise maneuvering of the AV. The LiDAR system generates high-resolution 3D point clouds to detect obstacles and map the surroundings. The radar system provides long-range detection and velocity measurements of objects, enhancing situational awareness. The stereo camera system captures depth-perception data, allowing for object recognition and tracking. The control system combines these inputs to determine the AV's position, identify obstacles, and adjust movement parameters such as speed and direction. This integration ensures the AV can navigate dynamically, avoiding collisions and adhering to the planned route while responding to real-time environmental changes. The system improves safety and reliability by leveraging redundant sensor data to validate and refine navigation decisions.
31. The system of claim 22 , wherein the control system dynamically processes data from the sensor array system to increase speed, reduce speed, or stop the AV along the current route.
The invention relates to autonomous vehicle (AV) control systems designed to enhance navigation and safety by dynamically processing sensor data to adjust vehicle movement. The system integrates a sensor array system that collects real-time data about the vehicle's environment, including obstacles, traffic conditions, and road characteristics. A control system processes this data to make real-time decisions, such as increasing speed, reducing speed, or stopping the vehicle along its current route. The control system evaluates sensor inputs to determine optimal actions, ensuring safe and efficient navigation. This dynamic processing allows the AV to respond to changing conditions without manual intervention, improving safety and operational efficiency. The system may also incorporate additional features, such as route planning and obstacle avoidance, to further enhance performance. By continuously analyzing sensor data, the AV can adapt its speed and movement to avoid collisions, comply with traffic rules, and optimize travel time. The invention addresses the need for reliable, real-time decision-making in autonomous vehicles to ensure safe and efficient operation in various environments.
32. The system of claim 22 , wherein the detected reflectance events comprise one or more surface features of an environment around the AV and one or more weather features indicating precipitation.
The system is designed for autonomous vehicles (AVs) to detect and analyze reflectance events in the surrounding environment, including surface features and weather conditions like precipitation. The system captures reflectance data from the environment, which may include road surfaces, obstacles, or other objects, as well as atmospheric conditions such as rain, snow, or fog. By processing this data, the system identifies and classifies these reflectance events to improve the AV's perception of its surroundings. This helps the AV navigate safely by distinguishing between different environmental factors that could affect visibility, traction, or sensor performance. The system may use sensors like LiDAR, cameras, or radar to gather reflectance data, and it applies algorithms to interpret the detected events in real time. The goal is to enhance the AV's ability to adapt to changing conditions, ensuring reliable operation in various weather scenarios and complex environments.
33. The system of claim 22 , wherein the executed instruction further causes the predictive sensor array configuration system to: in response to identifying the weather features indicating precipitation, deprioritize sensor data from the LiDAR system for processing by the control system.
This invention relates to a predictive sensor array configuration system for autonomous vehicles, addressing the challenge of optimizing sensor data processing in varying weather conditions. The system dynamically adjusts sensor prioritization based on real-time weather features to improve reliability and efficiency. Specifically, when precipitation is detected, the system deprioritizes LiDAR sensor data, reducing its influence on the vehicle's control system. This adjustment mitigates LiDAR performance degradation caused by rain, snow, or fog, which can scatter or absorb laser pulses, leading to inaccurate distance measurements. By deprioritizing LiDAR data under such conditions, the system relies more on alternative sensors like cameras or radar, which may perform better in precipitation. The system continuously monitors weather features and dynamically reconfigures sensor data processing to maintain optimal situational awareness and decision-making for the autonomous vehicle. This adaptive approach enhances safety and operational efficiency in adverse weather scenarios.
34. The system of claim 33 , wherein the executed instruction further causes the system to: in response to identifying the weather features indicating precipitation, deactivate the LiDAR system.
This invention relates to a system for managing a LiDAR (Light Detection and Ranging) system in response to weather conditions. The system monitors weather features, particularly precipitation, to determine when to deactivate the LiDAR system to prevent performance degradation or damage. The system includes a processor and memory storing instructions that, when executed, cause the system to analyze weather data to detect precipitation. Upon detecting precipitation, the system automatically deactivates the LiDAR system to avoid interference from rain, snow, or other precipitation that can distort LiDAR measurements. The system may also include a sensor or data feed to obtain real-time weather information, ensuring timely deactivation. This approach improves LiDAR reliability in adverse weather conditions by preventing erroneous data collection when visibility is compromised. The system may be integrated into autonomous vehicles, drones, or other applications where LiDAR is used for navigation or environmental sensing. By dynamically adjusting LiDAR operation based on weather, the system enhances safety and accuracy in outdoor environments.
35. The system of claim 34 , wherein the executed instruction causes the system to dynamically identify the one or more reflectance events by receiving reflectance data from a number of proximate AVs traveling on the current route on the railway system.
This invention relates to railway systems and addresses the challenge of dynamically identifying reflectance events, such as track conditions or obstructions, to improve safety and efficiency. The system collects reflectance data from multiple autonomous vehicles (AVs) traveling on the same railway route. By aggregating and analyzing this data in real-time, the system detects and identifies reflectance events, which may include track defects, debris, or other anomalies that could affect vehicle operation. The system processes the reflectance data to determine the location, severity, and type of each event, enabling timely maintenance or corrective actions. The use of multiple AVs provides a more comprehensive and accurate assessment of track conditions compared to relying on a single vehicle. This dynamic identification helps prevent accidents, reduces downtime, and enhances overall railway system reliability. The system integrates with existing railway infrastructure and AV sensors to provide continuous monitoring and real-time updates, ensuring safer and more efficient railway operations.
36. The system of claim 35 , wherein the executed instruction causes the system to receive the reflectance data from the proximate AVs by establishing a mesh network with the proximate AVs on the railway system.
The system involves autonomous vehicles (AVs) operating on a railway system, where the AVs collect and share reflectance data to enhance navigation and safety. Reflectance data, which measures the reflection of light or other signals from the railway environment, helps AVs detect tracks, obstacles, and other critical features. The system includes a mesh network that enables AVs to communicate reflectance data directly with nearby AVs, creating a distributed network for real-time data sharing. This mesh network allows AVs to operate without relying solely on centralized infrastructure, improving reliability and reducing latency. By exchanging reflectance data, AVs can improve their understanding of the railway environment, avoid collisions, and adapt to dynamic conditions. The system may also integrate additional sensors or processing units to refine data accuracy. The mesh network is designed to be scalable, allowing more AVs to join and contribute data as needed. This approach enhances the overall efficiency and safety of autonomous railway operations by leveraging collaborative sensing and communication.
37. The system of claim 22 , wherein the executed instruction further causes the system to: maintain a sub-map database comprising 3D surface data of an operational region of the AV on the railway system; and identify, using a current position of the AV, a correlated sub-map from the sub-map database that provides 3D surface data surrounding the current position of the AV; wherein the executed instruction causes the system to dynamically identify the reflectance events that affect detectability by the sensor array from the 3D surface data provided by the correlated sub-map.
This invention relates to autonomous vehicle (AV) navigation on railway systems, addressing challenges in detecting and mitigating reflectance events that affect sensor performance. The system uses a sub-map database containing 3D surface data of the operational region to enhance situational awareness. The database stores detailed surface characteristics of the railway environment, including potential reflectance sources like wet surfaces, shiny objects, or uneven terrain. As the AV moves, the system dynamically correlates its current position with the sub-map database to retrieve relevant 3D surface data surrounding the AV. This data is used to predict and identify reflectance events that could impair sensor detectability, such as glare or false readings from the sensor array. By leveraging pre-mapped surface information, the system improves sensor reliability and navigation accuracy in varying environmental conditions. The approach ensures real-time adaptation to reflectance challenges without relying solely on live sensor inputs, reducing errors in object detection and path planning. This method is particularly useful in railway environments where surface conditions can significantly impact sensor performance.
38. The system of claim 22 , wherein the stationary feature being one or more of a railway sign, a railway station, a railway track, a vehicle roadway, a railway crossing, or other fixture, the location of one of the other plurality of vehicles comprising a distance between one of the plurality of vehicle and the AV, the anomaly being one or more of a human being, a dog, a cat, a horse, cattle, a moving vehicle crossing the railway track, a weather condition, or a defect on the railway track.
This invention relates to an autonomous vehicle (AV) system designed to detect and respond to anomalies in railway environments. The system monitors stationary features such as railway signs, stations, tracks, roadways, crossings, and other fixtures to ensure safe navigation. It also tracks the locations of nearby vehicles, including the distance between other vehicles and the AV, to maintain situational awareness. The system identifies anomalies that could pose risks, such as pedestrians, animals (e.g., dogs, cats, horses, cattle), moving vehicles crossing tracks, adverse weather conditions, or defects on the railway. By continuously assessing these factors, the AV can adjust its operations to avoid collisions or disruptions. The system enhances safety by integrating real-time data from multiple sources, allowing the AV to make informed decisions in dynamic railway environments. This approach ensures reliable operation in complex and potentially hazardous conditions.
39. A method for moving an autonomous vehicle (AV) among a plurality of vehicles configured on a railway system, the method comprising: dynamically detecting a reflectance of an event from a plurality of events of the AV on a rail of the railway system using a sensor array system configured spatially on the AV that has a plurality of active sensor systems, the event being selected from an anomaly, a stationary feature, or a location of one or more of the plurality of vehicles; using data from the reflectance of the event or the plurality of events to adjust a movement of the AV in relationship to the event, while the AV is mechanically disconnected from the other vehicles in the plurality of vehicles; monitoring the AV and the other vehicles in the plurality of vehicles for information in real time; and; collecting the information in a central database.
The invention relates to autonomous vehicle (AV) navigation in railway systems, addressing challenges in dynamic environment sensing and real-time decision-making for AVs operating independently of other vehicles. The method involves using a sensor array system mounted on the AV to detect reflectance from various events on the railway, such as anomalies, stationary features, or the positions of other vehicles. The sensor array includes multiple active sensor systems spatially distributed on the AV to capture detailed reflectance data. This data is used to adjust the AV's movement relative to detected events, ensuring safe and efficient navigation while the AV remains mechanically disconnected from other vehicles. The system continuously monitors the AV and surrounding vehicles in real time, collecting operational data in a central database for analysis and optimization. The approach enhances situational awareness and adaptive control for autonomous railway vehicles, improving safety and operational efficiency in complex railway environments.
40. The method of claim 39 , further comprising communicating with a user for a user pickup.
A system and method for managing deliveries involves coordinating the pickup of items by users. The technology addresses inefficiencies in traditional delivery systems, such as delays, miscommunication, and lack of real-time tracking. The method includes tracking the location of a delivery vehicle carrying items to be picked up, determining an optimal pickup location based on the vehicle's route and the user's availability, and providing real-time updates to the user. The system also includes a user interface for scheduling, rescheduling, or canceling pickups, as well as notifying the user when the vehicle is nearby. Additionally, the method involves communicating with the user to confirm the pickup, ensuring the user is present at the designated location when the vehicle arrives. This enhances convenience, reduces wait times, and improves overall delivery efficiency. The system may also integrate with mapping services to optimize routes and provide accurate arrival estimates. The method ensures seamless coordination between the delivery vehicle and the user, minimizing logistical challenges and improving customer satisfaction.
41. The method of claim 39 , further comprising: a relationship table comprising a plurality of sensor configurations for each respective one of the plurality of active sensor systems to adjust within a time frame one or more output sensor configurations for the sensor array system to identify the event using the detected reflectance of the event; and an output to influence movement of the AV based upon the detected reflectance and identified event.
This invention relates to autonomous vehicle (AV) sensor systems designed to improve event detection and response. The system includes multiple active sensor systems that dynamically adjust their configurations to enhance the detection of events based on reflectance data. A relationship table stores multiple sensor configurations for each active sensor system, allowing the system to modify one or more output sensor configurations within a specified time frame. These adjustments optimize the sensor array system's ability to identify events by analyzing the detected reflectance of the event. The system then generates an output that influences the AV's movement based on the detected reflectance and the identified event. This approach ensures that the AV can accurately detect and respond to events in real time, improving safety and navigation. The dynamic adjustment of sensor configurations allows the system to adapt to varying environmental conditions and event characteristics, ensuring reliable event identification and appropriate AV responses.
42. The method of claim 41 , wherein the sensor array system further comprises a light-detection and ranging (LiDAR) system, and wherein the output sensor configuration configures one or more of a scan rate of the LiDAR system, a photodetector sensitivity of the LiDAR system, or a laser power level of the LiDAR system.
This invention relates to sensor array systems used for environmental monitoring, autonomous navigation, or object detection, addressing the need for adaptive sensor configurations to optimize performance in varying conditions. The system includes a LiDAR (light-detection and ranging) system integrated with other sensors to dynamically adjust operational parameters based on environmental factors or application requirements. The LiDAR system's scan rate, photodetector sensitivity, and laser power level can be configured to enhance accuracy, reduce power consumption, or improve detection range. By adjusting these parameters, the system adapts to different scenarios, such as low-light conditions, high-speed movement, or energy-efficient operation. The configuration may be pre-set or dynamically adjusted in real-time to optimize performance for specific tasks, such as obstacle avoidance in autonomous vehicles or precise environmental mapping. This adaptability ensures reliable and efficient sensor operation across diverse applications.
43. The method of claim 41 , wherein the sensor array system further comprises a radar system, and wherein the output sensor configuration configures a pulse width of a carrier signal of the radar system.
A method for configuring a sensor array system, particularly for radar-based applications, addresses the challenge of optimizing sensor performance in dynamic environments. The sensor array system includes a radar system that emits a carrier signal with adjustable parameters. The method involves dynamically configuring the sensor array, including the radar system, based on environmental conditions or operational requirements. Specifically, the configuration adjusts the pulse width of the radar system's carrier signal to enhance detection accuracy, reduce interference, or improve energy efficiency. The radar system may operate in various frequency bands and modulation schemes, and the pulse width adjustment ensures optimal signal processing for target identification and tracking. This configuration method allows the sensor array to adapt to changing conditions, such as varying target distances, clutter levels, or environmental noise, thereby improving overall system reliability and performance. The technique is applicable in automotive, aerospace, industrial, and surveillance applications where precise and adaptable radar sensing is critical.
44. The method of claim 41 , wherein each of the plurality of active sensor systems emits one or more of sounds waves or electromagnetic waves.
This invention relates to a system for monitoring and analyzing environmental conditions using multiple active sensor systems. The system addresses the need for accurate, real-time data collection in environments where passive sensors may be insufficient, such as in industrial, military, or scientific applications. The invention employs a network of active sensor systems that emit and detect signals, such as sound waves or electromagnetic waves, to gather detailed information about the surrounding environment. These sensors can be deployed in various configurations to cover a wide area or focus on specific regions of interest. The emitted signals interact with the environment, and the reflected or modified signals are analyzed to determine properties like distance, material composition, or movement. The system may include processing units that interpret the sensor data to generate actionable insights, such as detecting anomalies, tracking objects, or assessing structural integrity. The use of multiple active sensors enhances accuracy and reliability by providing redundant measurements and cross-verifying data. This approach improves situational awareness and decision-making in dynamic or hazardous environments. The invention may also incorporate adaptive algorithms to optimize sensor operation based on environmental conditions or mission requirements.
45. The method of claim 41 , wherein the executed instruction causes the system to dynamically determine the one or more output sensor configurations by performing a lookup in the relationship table based on a reflectance of each of the detected reflectance events.
A system and method for dynamically configuring output sensor configurations based on detected reflectance events. The technology addresses the challenge of optimizing sensor performance in environments where reflectance properties vary, such as in industrial automation, robotics, or optical sensing applications. The system detects reflectance events from a target surface, where each event corresponds to a specific reflectance property. A relationship table maps these reflectance properties to optimal output sensor configurations, such as gain settings, exposure times, or filtering parameters. The system dynamically determines the appropriate sensor configuration by performing a lookup in the relationship table using the reflectance value of each detected event. This ensures real-time adaptation to changing reflectance conditions, improving accuracy and efficiency in sensor-based measurements or imaging tasks. The relationship table may be pre-populated with empirically derived or algorithmically generated mappings, allowing the system to select configurations that enhance signal quality or reduce noise. The method enables adaptive sensing without manual intervention, making it suitable for automated processes where reflectance conditions fluctuate.
46. The method of claim 41 , wherein the executed instruction causes the system to dynamically determine the one or more output sensor configurations by performing an optimization utilizing a plurality of possible configurations for each of the plurality of active sensor systems based on a reflectance of each of the detected reflectance events by identifying a surface feature of each of the events, and wherein the optimization uses a fitting function to converge on the one or more sensor output configurations in the relationship table.
This invention relates to dynamic sensor configuration optimization in systems that detect reflectance events from surfaces. The problem addressed is the need to efficiently determine optimal sensor configurations for accurately capturing and analyzing reflectance data from various surface features. The method involves dynamically adjusting sensor configurations based on detected reflectance events, improving data accuracy and system performance. The system includes multiple active sensor systems that detect reflectance events from surfaces. Each event is analyzed to identify specific surface features, such as texture, material, or geometry. The method then evaluates a plurality of possible configurations for each sensor system, considering the reflectance properties of the detected events. An optimization process is applied, using a fitting function to refine the sensor configurations. The fitting function iteratively adjusts the configurations to minimize errors and maximize accuracy in capturing the surface features. The optimized configurations are stored in a relationship table, which maps the best-performing settings for different reflectance scenarios. This approach ensures that the sensor systems adapt in real-time to varying surface conditions, enhancing the reliability and precision of reflectance measurements. The dynamic optimization reduces manual calibration efforts and improves system efficiency in applications such as industrial inspection, material analysis, or environmental monitoring.
47. The method of claim 41 wherein the plurality of active sensor systems comprises a light-detection and ranging (LiDAR) system and a radar system, and wherein the sensor array system further includes a plurality of passive sensor systems that detect reflected natural light.
This invention relates to advanced sensor systems for environmental perception, particularly in autonomous vehicles or robotic systems. The technology addresses the challenge of accurately detecting and interpreting surroundings using multiple sensor modalities to improve situational awareness and decision-making in dynamic environments. The method involves a sensor array system that integrates both active and passive sensor systems. Active sensor systems, such as a light-detection and ranging (LiDAR) system and a radar system, actively emit signals (e.g., laser pulses or radio waves) to measure distances and velocities of objects in the environment. The LiDAR system provides high-resolution 3D mapping, while the radar system detects moving objects and measures their speed. Passive sensor systems, which detect reflected natural light, complement these active sensors by capturing visual data without emitting signals, reducing power consumption and avoiding interference. The combined use of active and passive sensors enhances reliability and accuracy by cross-referencing data from different sources. For example, LiDAR and radar can detect obstacles and their motion, while passive sensors provide additional contextual information, such as object shapes and textures. This multi-modal approach improves robustness in varying conditions, such as low light or adverse weather, where individual sensors may perform suboptimally. The system is particularly useful in autonomous navigation, where real-time, high-fidelity environmental perception is critical for safe operation.
48. The method of claim 47 , wherein the plurality of passive sensor systems of the sensor array system comprises a stereo camera system.
The invention relates to a sensor array system for environmental monitoring, particularly for detecting and analyzing objects or conditions in a surrounding environment. The system addresses the challenge of accurately capturing and interpreting environmental data using multiple passive sensor systems, which do not emit signals but instead rely on ambient energy (e.g., light, sound) to gather information. The system integrates these sensors to enhance detection capabilities, reduce power consumption, and improve reliability compared to active sensors that emit signals. The sensor array system includes a plurality of passive sensor systems arranged to collect environmental data from different perspectives or modalities. These sensors may include cameras, microphones, or other passive devices that capture ambient signals without emitting energy. The system processes the collected data to generate a comprehensive environmental model, enabling applications such as object detection, tracking, or environmental mapping. In one embodiment, the passive sensor systems include a stereo camera system, which captures depth information by analyzing images from two or more cameras. This allows for three-dimensional reconstruction of the environment, improving object localization and spatial awareness. The stereo camera system may be combined with other passive sensors, such as thermal or acoustic sensors, to provide a more detailed environmental analysis. The system may also incorporate machine learning algorithms to interpret sensor data and identify patterns or anomalies in real time. The overall design aims to provide a robust, energy-efficient solution for environmental monitoring in applications like autonomous navigation, surveillance, or industrial automation.
49. The method of claim 48 wherein the LiDAR system, the radar system, and the stereo camera system each provides sensor data to a control system of the AV to enable the AV to maneuver along the rail and initiate adjustment of the movement of the AV along a track.
This invention relates to autonomous vehicle (AV) navigation systems for guiding vehicles along predefined tracks or rails. The system addresses the challenge of accurately maneuvering an AV along a track by integrating multiple sensor inputs to ensure precise movement and real-time adjustments. The AV is equipped with a LiDAR system, a radar system, and a stereo camera system, each providing distinct sensor data to a central control system. The LiDAR system generates high-resolution 3D point cloud data to map the environment and detect obstacles, while the radar system provides long-range detection and velocity measurements of surrounding objects. The stereo camera system captures stereo images to enhance depth perception and object recognition. The control system processes the combined sensor data to determine the AV's position relative to the track, detect deviations, and adjust the vehicle's movement accordingly. This integration allows the AV to navigate complex track geometries, avoid obstacles, and maintain optimal speed and alignment. The system ensures reliable and adaptive navigation, improving safety and efficiency in autonomous track-based transportation.
50. The method of claim 49 , wherein the control system dynamically processes data from the sensor array system to increase speed, reduce speed, or stop the AV along the rail or wherein the control system interfaces with the central database to increase speed, reduce speed, or stop the AV along the rail.
This invention relates to an automated vehicle (AV) control system for rail-based transportation. The system addresses the challenge of dynamically managing AV operations to ensure safety, efficiency, and responsiveness to real-time conditions. The AV is equipped with a sensor array system that collects data on environmental factors, vehicle status, and rail conditions. A control system processes this data to adjust the AV's speed or stop it as needed. Additionally, the control system interfaces with a central database to access additional data, such as traffic conditions, maintenance schedules, or emergency alerts, to further optimize AV performance. The dynamic processing of sensor data and database interactions enables real-time decision-making, improving operational safety and efficiency. The system ensures that the AV can respond to obstacles, track hazards, or system malfunctions by adjusting speed or stopping when necessary. This approach enhances reliability and adaptability in rail-based AV operations.
51. The method of claim 50 , wherein the detected reflectance events comprise one or more surface features of an environment around the AV and one or more weather features indicating precipitation.
This invention relates to autonomous vehicle (AV) systems that detect and analyze reflectance events in the environment to improve navigation and safety. The method involves processing sensor data to identify surface features of the environment, such as road markings, obstacles, or terrain characteristics, as well as weather-related reflectance events, such as rain, snow, or fog. By distinguishing between these different types of reflectance events, the AV can adapt its perception and decision-making processes. For example, surface features may be used for localization or path planning, while weather features help adjust sensor calibration or activate weather-specific driving modes. The system may use light detection and ranging (LiDAR) or other optical sensors to capture reflectance data, which is then processed to classify and interpret the detected events. This approach enhances the AV's ability to operate reliably in varying environmental conditions, reducing the risk of misinterpretation of sensor data due to weather interference. The method may also integrate with other AV subsystems, such as mapping or control systems, to provide a more comprehensive understanding of the driving environment.
52. The method of claim 51 , wherein the executed instruction further causes the predictive sensor array configuration system to: in response to identifying the weather features indicating precipitation, deprioritize sensor data from the LiDAR system for processing by the control system.
This invention relates to a predictive sensor array configuration system for autonomous vehicles, addressing the challenge of optimizing sensor data processing in varying weather conditions. The system dynamically adjusts sensor prioritization based on real-time weather features to improve vehicle control and safety. Specifically, when weather features indicate precipitation, the system deprioritizes sensor data from the LiDAR system for processing by the control system. This adjustment helps mitigate the degradation of LiDAR performance in rain or snow, ensuring more reliable sensor inputs for navigation and decision-making. The system continuously monitors weather conditions and sensor performance to adapt configurations in real time, enhancing overall system robustness. By selectively prioritizing or deprioritizing sensor data based on environmental factors, the invention improves the efficiency and accuracy of autonomous vehicle operations in adverse weather. The method involves analyzing weather data, identifying precipitation-related features, and adjusting sensor data processing priorities accordingly. This approach ensures that the control system receives the most relevant and reliable sensor inputs for safe and effective vehicle operation.
53. The method of claim 52 , wherein the executed instruction further causes the system to: in response to identifying the weather features indicating precipitation, deactivate the LiDAR system.
This invention relates to autonomous vehicle systems that use LiDAR (Light Detection and Ranging) sensors for navigation and obstacle detection. The problem addressed is the degradation of LiDAR performance in adverse weather conditions, particularly precipitation, which can scatter or absorb laser pulses, leading to inaccurate or unreliable data. The solution involves dynamically adjusting the LiDAR system's operation based on detected weather conditions to improve reliability and safety. The system monitors weather features, such as precipitation, using sensors or external data sources. When precipitation is detected, the LiDAR system is automatically deactivated to prevent erroneous readings. This ensures that the autonomous vehicle relies on alternative sensors or systems, such as radar or cameras, which may be less affected by rain or snow. The method enhances the vehicle's ability to navigate safely in varying weather conditions by reducing reliance on compromised LiDAR data during precipitation. The system may also reactivate the LiDAR when weather conditions improve, ensuring optimal sensor usage. This approach improves the robustness of autonomous vehicle navigation in dynamic environments.
54. The method of claim 41 , wherein the executed instruction causes the system to dynamically identify the one or more reflectance events by receiving reflectance data from a number of proximate AVs traveling on the railway system.
This invention relates to railway systems and addresses the challenge of dynamically identifying reflectance events, which are critical for ensuring safe and efficient train operations. Reflectance events occur when light or signals are reflected in unexpected ways, potentially causing misinterpretation by automated systems or human operators. The invention involves a method for detecting these events by analyzing reflectance data collected from multiple autonomous vehicles (AVs) operating on the railway system. The AVs are equipped with sensors that capture reflectance data as they travel along the tracks. By aggregating and processing this data from multiple AVs, the system can dynamically identify reflectance events in real-time, allowing for immediate adjustments to avoid operational disruptions. The method leverages the collective data from proximate AVs to enhance accuracy and reliability in detecting reflectance anomalies. This approach improves safety by reducing the risk of misinterpreted signals and ensures smoother railway operations by proactively addressing reflectance-related issues. The system dynamically processes the reflectance data to identify patterns or deviations that indicate potential reflectance events, enabling timely corrective actions. The use of multiple AVs provides a more comprehensive and robust detection mechanism compared to relying on a single vehicle's data. This method is particularly useful in complex railway environments where reflectance conditions can vary significantly.
55. The method of claim 54 , wherein the executed instruction causes the system to receive the reflectance data from the proximate AVs by establishing a mesh network with the proximate AVs on the railway system or other entities that are not AVs.
This invention relates to autonomous vehicle (AV) systems operating on railway networks, focusing on improving situational awareness and safety through enhanced data sharing. The problem addressed is the limited ability of individual AVs to gather comprehensive environmental data, particularly in dynamic railway environments where obstacles, track conditions, or other AVs may pose risks. The solution involves a method where an AV receives reflectance data from nearby AVs or other non-AV entities to enhance its perception of the surroundings. The reflectance data, which may include information about obstacles, track conditions, or other relevant environmental factors, is shared via a mesh network. This network allows AVs to communicate directly with each other and with non-AV entities, such as infrastructure sensors or control systems, to create a more robust and distributed sensing system. By integrating this data, an AV can make more informed decisions, improving safety and efficiency in railway operations. The mesh network ensures reliable data transmission even in areas with limited infrastructure, as each AV or entity can act as a relay node, extending the network's reach. This approach enhances the overall situational awareness of the AV system, reducing the risk of collisions and improving operational reliability.
56. The method of claim 41 , wherein the executed instruction further causes the system to: maintain a sub-map database comprising 3D surface data of an operational region of the AV on the railway system; and identify, using a current position of the AV, a correlated sub-map from the sub-map database that provides 3D surface data surrounding the current position of the AV; wherein the executed instruction causes the system to dynamically identify the reflectance events that affect detectability by the sensor array from the 3D surface data provided by the correlated sub-map.
This invention relates to autonomous vehicle (AV) navigation on railway systems, specifically addressing challenges in sensor-based detection of obstacles or track conditions. The system uses a sensor array to detect reflectance events, which are light or signal reflections that can obscure or distort sensor readings. To improve detection accuracy, the system maintains a sub-map database containing detailed 3D surface data of the operational region of the AV on the railway. This database includes high-resolution surface characteristics of the tracks and surrounding environment. As the AV moves, the system dynamically identifies its current position and retrieves a correlated sub-map from the database that provides 3D surface data surrounding that position. The system then analyzes this data to determine how reflectance events from the 3D surfaces may affect the detectability of obstacles or track conditions by the sensor array. By correlating real-time sensor data with pre-mapped surface information, the system enhances obstacle detection and navigation reliability in varying environmental conditions. The approach reduces false positives and improves the AV's ability to navigate safely by accounting for surface-induced reflectance effects.
57. The method of claim 41 , wherein the stationary feature is one or more of a railway sign, a railway station, a railway track, a vehicle roadway, a railway crossing, or other fixture, the location of one of the other plurality of vehicles comprising a distance between the other vehicle and the AV, the anomaly being one or more of a human being, a dog, a cat, a horse, cattle, a moving vehicle crossing the railway track, a weather condition, or a defect on the railway track.
This invention relates to autonomous vehicle (AV) systems for railway environments, addressing the challenge of detecting and responding to anomalies that could disrupt safe operation. The system uses sensors to monitor the AV's surroundings, including stationary features like railway signs, stations, tracks, roadways, crossings, and other fixtures. It also tracks the positions of nearby vehicles, measuring their distance from the AV. The system identifies anomalies such as pedestrians, animals (e.g., dogs, cats, horses, cattle), moving vehicles crossing tracks, weather conditions, or track defects. Upon detecting an anomaly, the system generates alerts or triggers corrective actions to ensure safe navigation. The AV may adjust its speed, route, or other operational parameters based on the detected anomaly's type, location, and potential impact. This approach enhances safety by proactively addressing dynamic and static hazards in railway environments, ensuring reliable AV operation in complex and unpredictable conditions.
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April 7, 2020
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